Steel and Composite Structures

  • 제50권4호
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  • Pages.443-458
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  • 2024
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  • 1229-9367(pISSN)
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  • 1598-6233(eISSN)
테크노프레스 (Techno-Press)
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Application of a comparative analysis of random forest programming to predict the strength of environmentally-friendly geopolymer concrete

  • Ying Bi (School of Civil Engineering and Architecture, Zhengzhou Shengda University of Economics, Business & Management) ;
  • Yeng Yi (Department of Civil Engineering, Huazhong University)
  • 투고 : 2023.08.26
  • 심사 : 2023.01.24
  • 발행 : 2024.02.25
⟨ 이전 논문 다음 논문 ⟩

초록

The construction industry, one of the biggest producers of greenhouse emissions, is under a lot of pressure as a result of growing worries about how climate change may affect local communities. Geopolymer concrete (GPC) has emerged as a feasible choice for construction materials as a result of the environmental issues connected to the manufacture of cement. The findings of this study contribute to the development of machine learning methods for estimating the properties of eco-friendly concrete, which might be used in lieu of traditional concrete to reduce CO2 emissions in the building industry. In the present work, the compressive strength (fc) of GPC is calculated using random forests regression (RFR) methodology where natural zeolite (NZ) and silica fume (SF) replace ground granulated blast-furnace slag (GGBFS). From the literature, a thorough set of experimental experiments on GPC samples were compiled, totaling 254 data rows. The considered RFR integrated with artificial hummingbird optimization (AHA), black widow optimization algorithm (BWOA), and chimp optimization algorithm (ChOA), abbreviated as ARFR, BRFR, and CRFR. The outcomes obtained for RFR models demonstrated satisfactory performance across all evaluation metrics in the prediction procedure. For R2 metric, the CRFR model gained 0.9988 and 0.9981 in the train and test data set higher than those for BRFR (0.9982 and 0.9969), followed by ARFR (0.9971 and 0.9956). Some other error and distribution metrics depicted a roughly 50% improvement for CRFR respect to ARFR.

키워드

과제정보

This work was supported by Henan Provincial Department of Science and Technology Science and Technology Research Program Project, 212102310284, Research on seismic performance of prefabricated shear walls connected to vertical steel structures.

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